Study of characteristics of the vasculature of eye provides useful insights to many ophthalmic disorders. The properties of vasculature such as width, length, branching pattern, and ratio of size of optic disk to size of optic cup are important parameters in detection of retinopathies, hypertension, glaucoma, and vein or artery occlusion. Early and regular screening of eye can help in timely detection and treatment of most of these problems. The manual examination of a large number of retinas by doctors to search for the symptoms is a labor intensive process. It has been established that automatic delineation of blood vessels in a retina can be very helpful in diagnosing, screening and treatment of such diseases. Thus, automatic extraction of retinal maps forms an important first step in establishing any computer aided diagnostic system for ophthalmic disorders.
Numerous methods have been used to segment the blood vessels from retina color images. These methods can be classified as supervised and unsupervised. Supervised methods usually have higher accuracy than unsupervised variants but require generation of high quality training data. Unsupervised methods have no such requisites and thus find greater application where training data is difficult to produce. A supervised learning algorithm using a large number of features for training has been proposed. This proposed methodology is based on ridge extraction to find location of vessels. The ridges are used to compose primitives in the form of line segments. The image is then partitioned into patches by assigning each pixel to the nearest line segment. Feature vectors are calculated for each pixel and classified using a KNN classifier. Another prior art technique uses Gabor feature based supervised learning for vessel segmentation. A Morlet wavelet transform has been used to extract features at different scales. Another technique uses grey intensity values and Hu's moments calculated on the image as features, and these are fed to a neural network for classification. In another technique, line detectors and Support Vector Machines are used to classify pixel as vessel or non-vessel. Recently, a vector divergence field approach has been proposed to handle bright lesions and obtained high accuracy. Another technique uses response towards Gaussian profile and first order derivative of Gaussian filter to differentiate between vessel and non-vessel regions. Yet others have used pixel classification methods to segment image into vessels and non-vessels.